Imitation Learning (IL) [1]} provides a simple and stable approach to learning robot behavior, but requires access to demonstrations. Collecting expert demonstrations of precise goal targeting in such a high speed setting, say from teleoperation or kinesthetic teaching [2]} is a complex engineering problem. Attempting to learn precise table tennis by trial and error using reinforcement learning (RL) is a similarly difficult proposition given its sample inefficiency and that the random exploration that is typical at the beginning stages of RL may damage the robot. High-frequency control also results in long horizon episodes. These are among the biggest challenges facing current deep RL techniques [3]}. While many recent RL approaches successfully learn in simulation, then transfer to the real world [4]}, [5]}, doing so in this setting remains difficult especially considering the requirement of precise, dynamic control. Here we restrict our focus to learning a hard dynamic problem directly on a physical robot without involving the complexities of sim-to-real transfer.
